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Fulltime Reinforcement Learning Phd Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

The Senior Reinforcement Learning Engineer will leverage their expertise in reinforcement learning ... • A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

The Senior Reinforcement Learning Engineer will focus on achieving state-of-the-art performance on ... • A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

The Senior Reinforcement Learning Engineer will focus on achieving state-of-the-art performance on ... • A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving ... A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience ...

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Fulltime Reinforcement Learning Phd information

What is the difference between Fulltime Reinforcement Learning Phd vs Machine Learning Engineer?

AspectFulltime Reinforcement Learning PhdMachine Learning Engineer
Required CredentialsPhD in Computer Science, AI, or related fieldBachelor's or Master's in CS, AI, or related field
Work EnvironmentResearch-focused, academic or R&D labsIndustry, product development teams
Employer & Industry UsageUniversities, research institutions, tech companiesTech companies, startups, enterprise firms
Common Search & ComparisonYesNo

Fulltime Reinforcement Learning Phds typically focus on research and theoretical development in AI, often working in academic or R&D settings. Machine Learning Engineers apply AI techniques to develop practical applications in industry. While both roles require strong AI knowledge, the Phd emphasizes research, whereas the Engineer emphasizes implementation.

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Infographic showing various Fulltime Reinforcement Learning Phd job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Applied Deep Learning PhD Research Intern, Reinforcement Learning for LLMs - Fall 2026

Applied Deep Learning PhD Research Intern, Reinforcement Learning for LLMs - Fall 2026

Nvidia

Santa Clara, CA

$17.50 - $23.50/hr

Full-time

Posted yesterday

Job description

We are looking for PhD research interns excited to advance the next generation of large language models through reinforcement learning. Our applied deep learning research team at NVIDIA has helped pioneer projects such as Megatron, MT-NLG, and DLSS. We build state-of-the-art foundation models and develop new methods to improve their reasoning, alignment, reliability, and ability to solve real-world tasks.

This internship will focus on algorithmic research at the intersection of reinforcement learning and large language models. You will design, implement, and evaluate new RL-based methods for improving LLM behavior, with a strong emphasis on hands-on experimentation and rapid prototyping at scale.

What you will be doing:

  • Develop and prototype reinforcement learning algorithms for large language models

  • Explore methods for improving reasoning, alignment, instruction following, and multi-turn interaction

  • Design experiments to evaluate model behavior, robustness, hallucination, and task performance

  • Implement research ideas in Python and PyTorch, and run experiments on large-scale GPU clusters

What we need to see:

  • Pursuing a PhD in AI, ML, CS, CE, EE, Math, Physics, or a related field

  • Strong background in reinforcement learning and natural language processing

  • Excellent programming skills, especially in Python

  • Experience with deep learning frameworks such as PyTorch

  • Comfort with experimental research, debugging models, and working with large-scale training pipelines

Ways to stand out from the crowd:

  • Publications or open-source contributions in RL, LLMs, alignment, reasoning, or post-training

  • Experience with RLHF, RLAIF, policy optimization, reward modeling, or agentic LLM systems

  • Strong intuition for both algorithms and large-scale implementation

If you are excited about using reinforcement learning to make language models more capable, reliable, and useful, this team could be a great fit.

Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 30 USD - 94 USD.

You will also be eligible for Internbenefits.

Applications for this job will be accepted at least until May 10, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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